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Hydrogen Bonds

Hydrogen Bonds are fassinating and crucial non-covalent interactions that play a pivotal role in many biological and chemical processes.
These directional, electrostatic attractions between a hydrogen atom and an electronegative atom, such as oxygen or nitrogen, are essential for stabilizing molecular structures and mediating important intermolecular interactions.
Understanding and accurately identifying hydrogen bond patterns is vital for research in fields like structural biology, drug design, and materials science.
PubCompare.ai offers a seamless solution, empowering researchers to easily locate the best hydrogen bond protocols from literature, preprints, and patents using AI-driven comparisons.
This innovative tool can help enhance research reproducibility by optimizing hydrogen bond analysis, making it easier than ever to identify the most accurate and reliable protocols.

Most cited protocols related to «Hydrogen Bonds»

Protein Simulation with CHARMM-GUI and NAMD

Protein simulation systems were prepared with the CHARMM-GUI.^{28 (link)} Briefly, protein structures taken from corresponding protein data bank^{29 (link)} files were solvated in pre-equilibrated cubic TIP3P water boxes of suitable sizes and counter-ions were added to keep systems neutral as detailed in Table 1. Periodic boundary conditions were applied and Lennard-Jones (LJ) interactions were truncated at 12 Å with a force switch smoothing function from 10 Å to 12 Å. The non-bonded interaction lists were generated with a distance cutoff of 16 Å and updated heuristically. Electrostatic interactions were calculated using the particle mesh Ewald method³⁰ with a real space cutoff of 12 Å on an approximately 1 Å grid with 6th order spline. Covalent bonds to hydrogen atoms were constrained by SHAKE.³¹ After a 200 step Steepest Descent (SD) minimization with the protein fixed and another 200 steps without the protein fixed, the systems were first heated to 300 K and then subjected to a 100 ps NVT simulation followed by a 100 ps NPT simulation. The minimization, heating and initial equilibrium was performed with CHARMM,^{32 (link)} and the resultant structures were used to start simulations in NAMD.^{33 (link)} After a 1 ns NPT simulation as equilibration, the production simulations were run for 100 ns in the NVT ensemble (see Table 1). For HEWL NPT ensembles were generated to better compare with previous work that found CMAP helps to better reproduced order parameter S^{2,34 (link)} and simulations were extended to 200 ns to reduce the uncertainty of the computed S². Langevin thermostat with a damping factor of 5 ps⁻¹ was used for NVT simulation and the Nosé-Hoover Langevin piston method with a barostat oscillation time scale of 200 fs was further applied for the NPT simulation at 300 K and 1 atm. The time step equals 2 fs and coordinates were stored every 10 ps. For each protein the above simulation protocol was applied with the C36 and C22/CMAP FFs, while for ubiquitin an additional 1.2 μs trajectories with C36 was generated. This long simulation is used to check the convergence and also to examine whether computed NMR data deteriorate over a longer simulation time, as it was reported that RDCs significantly deviate from experimental values after approximately 500 ns simulations with the C22 FF.^{22 (link)}

Huang J, & MacKerell AD J.r. (2013). CHARMM36 all-atom additive protein force field: Validation based on comparison to NMR data. Journal of computational chemistry, 34(25), 2135-2145.

Publication 2013

Cuboid Bone Electrostatics factor A Factor V Familial Mediterranean Fever Hydrogen Bonds Ions Proteins Ring dermoid of cornea Staphylococcal Protein A STEEP1 protein, human Tremor Ubiquitin

Protein Structural Alignment Using SS and TM-Score

Three kinds of quickly identified initial alignments are exploited. The first type of initial alignment is obtained by aligning the secondary structures (SSs) of two proteins using dynamic programming (DP) (19 (link)). The element of the score matrix is assigned to be 1 or 0 depending on whether or not the SS elements of aligned residues are identical. Here, a penalty of −1 for gap-opening works the best. For a given residue, an SS state (α, β or coil) is assigned based on the C_α coordinates of five neighboring residues, i.e. ith residue is assigned as α(β) when

| d_{j, j + k} - λ_{k}^{α (β)} | < δ^{α (β)}, (j = i - 2, i - 1, i; k = 2, 3, 4)

is satisfied for all d_j,j+k that denotes the C_α distance between the jth and (j + k)th residues; otherwise, it is assigned to be a coil. The final assignment is further smoothed by merging and removing singlet SS states. We note that the set of eight parameters are optimized based on 100 non-homologous training proteins by maximizing the SS assignment similarity to the DSSP definition (20 (link)), which defines protein SS elements on the basis of hydrogen bond patterns and requires the full set of backbone atomic coordinates. The optimized parameters are

λ_{2}^{α} = 5.45 Å

λ_{3}^{α} = 5.18 Å

λ_{4}^{α} = 6.37 Å

, δ^α = 2.1 Å,

λ_{2}^{β} = 6.1 Å

λ_{3}^{β} = 10.4 Å

λ_{4}^{β} = 13 Å

, δ^β = 1.42 Å. Using Equation 1, we achieve an average Q₃ accuracy of 85% with respect to the DSSP assignment for the representative 1489 non-homologous test protein set used in Ref. (8 (link)).
The second type of initial alignment is based on the gapless matching of two structures. As in SAL (18 (link)), for the smaller of the two compared proteins, we perform gapless threading against the larger structure, but rather than use RMSD as the comparison metric as was done in SAL, now the alignment with the best TM-score is selected.
The third initial alignment is also obtained by DP using a gap-opening penalty of −1, but the score matrix is a half/half combination of the SS score matrix and the distance score matrix selected in the second initial alignment.

Zhang Y, & Skolnick J. (2005). TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Research, 33(7), 2302-2309.

Publication 2005

Hydrogen Bonds Proteins SET protein, human Vertebral Column

Structural Model Refinement Techniques

In restrained refinement, extra information is introduced through the term T_restraints with some weight (1). This extra term restrains model parameters to be similar, but not necessarily identical, to some reference values. At high to medium resolutions of approximately 3 Å or better, a standard set of restraints as implemented in PHENIX includes (Grosse-Kunstleve & Adams, 2004 ▸ (no links found)) restraints on covalent bond lengths and angles, dihedral angles, planarity and chirality restraints, and a nonbonded repulsion term. However, at lower resolutions the amount of experimental data is insufficient to preserve the geometry characteristics of a higher level of structural organization (such as secondary structure), and therefore including extra information (restraints or constraints) to help to produce a chemically meaningful model is desirable. These extra restraints or constraints may include similarity of related copies (NCS in the case of crystallography), restraints on secondary structure and restraints to one or more external reference models (for implementation details in PHENIX, see Headd et al., 2012 ▸ , 2014 ▸ ; Sobolev et al., 2015 ▸ ). phenix.real_space_refine can use the following extra restraints and constraints.

(i) Distance and angle restraints on hydrogen-bond patterns for protein helices and sheets and DNA/RNA base pairs.

(ii) Torsion-angle restraints on idealized protein secondary-structure fragments.

(iii) Restraints to maintain stacking bases in RNA/DNA parallel.

(iv) Ramachandran plot restraints.

(v) Amino-acid side-chain rotamer-specific restraints.

(vi) C^β deviation restraints.

(vii) Reference-model restraints, where a reference model may be a similar structure of better quality or the initial position of the model being refined.

(viii) Similarity restraints in torsion or Cartesian space.

(ix) NCS constraints.

Afonine P.V., Poon B.K., Read R.J., Sobolev O.V., Terwilliger T.C., Urzhumtsev A, & Adams P.D. (2018). Real-space refinement in PHENIX for cryo-EM and crystallography. Acta Crystallographica. Section D, Structural Biology, 74(Pt 6), 531-544.

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Publication 2018

Amino Acids Cardiac Arrest Crystallography Disgust Helix (Snails) Hydrogen Bonds Morphogenesis Proteins

Molecular Dynamics Simulations of Alanine and Glycine Peptides

Ala₃, Ala₅, Ala₇, Val₃, and Gly₃ were simulated in the NPT ensemble at 298K and 1 atm pressure under periodic boundary conditions. All peptides were unblocked and had protonated C-termini (experimental pH is ~2)^{31 (link)}. Initial box sizes were 32.13 Å³, 34.56 Å³, and 38.34 Å³ for tri-, penta-, and heptapeptides, respectively. PME summation³² was used to calculate the electrostatic interactions with a real-space cutoff set to 12 Å and a 1 Å grid spacing while the LJ interactions were treated with a switching function from 10 to 12 Å. The equations of motion were integrated with a 2 fs time step while SHAKE was used to constrain covalent bonds involving non-water hydrogen bonds and SETTLE³³ was used to maintain rigid water geometries. All of the peptides were simulated for 400 ns each with the new force field. In addition, Ala₅ was also simulated for 200 ns with the previous C22/CMAP force field^{16 (link)}, Amber ff99SB^{9 (link)}, Amber ff99SB*^{7 (link)}, OPLS-AA³⁴, and Gromos 53a6^{35 (link)}. All of the simulations were carried out with NAMD version 2.7b2. The equilibration protocol for all of the simulations consisted of initial minimization followed by step-wise heating to 298K. Simulations of zwitterionic GPGG were run using GROMACS with a 30 Å cubic box for 100 ns at 300 K, using the same non-bonded treatment, thermostat and barostat as those for Ac-(AAQAA)₃-NH₂ below.

Best R.B., Zhu X., Shim J., Lopes P.E., Mittal J., Feig M, & MacKerell AD J.r. (2012). Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ1 and χ2 dihedral angles. Journal of chemical theory and computation, 8(9), 3257-3273.

Publication 2012

Amber Cuboid Bone Electrostatics Familial Mediterranean Fever Hydrogen Bonds Muscle Rigidity natural heparin pentasaccharide Peptides Pressure Tremor

Visualizing Ligand Binding Site Properties

Autodock uses interaction maps for docking. Prior to the actual docking run these maps are calculated by the program autogrid. For each ligand atom type, the interaction energy between the ligand atom and the receptor is calculated for the entire binding site which is discretized through a grid. This has the advantage that interaction energies do not have to be calculated at each step of the docking process but only looked up in the respective grid map. In addition to speeding up a docking runs the grid maps on their own can also provide value hints for ligand optimization. Since a grid map represents the interaction energy as a function of the coordinates their visual inspection may reveal potential unsaturated hydrogen acceptors or donors or unfavourable overlaps between the ligand and the receptor. The plugin therefore provides the functionality to visualize these grid maps in PyMOL. The maps generated by autogrid are converted to a file format readable by PyMOL (DX format) which allows to draw isosurfaces and isomeshes analogous to electron density maps. Since several maps can be loaded and controlled simultaneously, a rapid inspection of several interaction types is made very easily. Figure 3 shows how these grid maps can be controlled via the plugin.Fig. 3

Autodock grid maps displayed with different contour levels. a Map for interactions of aliphatic carbon atoms at contour level 5 kcal/mol. b Same map at contour level −0.3 kcal/mol. c Hydrogen bond donor map at contour level −0.5 kcal/mol

In Fig. 3A an isosurface at a contour level of 5 kcal/mol for the interaction of the protein with aliphatic carbon atoms is shown. Such a setting may be used to get a visual impression of the overall shape of the binding site. Ligand modifications which cause a penetration of such a wall will most likely not enhance the affinity. In Fig. 3B the same map is visualized at a contour level of −0.3 kcal/mol. As can be seen, the shape of the surface, here shown as isomesh, roughly describes an envelope of the ligand and reveals putative spots of attractive interactions that may guide further ligand optimization. Likewise, hydrogen bond donor or acceptor interaction maps can guide ligand optimization since they might reveal unsaturated acceptor or donor positions (Fig. 3C).
The plugin provides functionality to handle different interaction maps and representations at different contour levels at the same time and hence, offers the possibility to visualize different binding site properties which may provide valuable insights for structure-based drug design.

Seeliger D, & de Groot B.L. (2010). Ligand docking and binding site analysis with PyMOL and Autodock/Vina. Journal of Computer-Aided Molecular Design, 24(5), 417-422.

Publication 2010

Binding Sites Carbon Donors Electrons Exanthema Hydrogen Hydrogen Bonds Ligands Microtubule-Associated Proteins Nuclear Energy Proteins Tissue Donors Vision

Most recents protocols related to «Hydrogen Bonds»

Molecular Modeling of ALK Inhibitor Compound 6

Not available on PMC !

Example 13

Molecular modeling study based upon the co-crystal structure of ALK with Alectinib (PDB: 3AOX) (Sakamoto, H. et al., Cancer Cell 2011, 19, 679) was performed to assess the structure-activity relationship of inhibition of ALK and/or ALK mutants by the compounds of the present application. The modeling showed that Compound 6 makes the same backbone hinge contact as Alectinib, however, Compound 6 forms two additional hydrogen bond interactions between the guanidine moiety of R1120 and the carbonyl group of the dimethyl acetamide group (FIG. 1A). Furthermore, in the G1202R mutant, Compound 6 forms an additional hydrogen bond interaction between the guanidine moiety of R1202 and the nitrogen of the pyrazole ring (FIG. 1B). The modeling study predicted that the methylene spacer between the pyrazole ring and the dimethylacetamide moiety is preferable for the carbonyl amide of Compound 6 to interact with the guanidine moiety of R1120.

US11858897B2. Substituted 6,11-dihydro-5H-benzo[b]carbazoles as inhibitors of ALK and SRPK (2024-01-02). DANA-FARBER CANCER INSTITUTE, INC. [US]. Inventors: John Hatcher [US], Nathanael S. Gray [US], Hwangeun Choi [KR], Pasi Janne [US], Tinghu Zhang [US].

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Patent 2024

alectinib Amides carbene Cells dimethylacetamide Guanidine Hydrogen-6 Hydrogen Bonds Malignant Neoplasms Nitrogen Psychological Inhibition pyrazole Vertebral Column

Molecular Complexity Analysis of Phytochemicals

The molecular complexity of the
phytochemicals in IMPPAT 2.0 was compared with four chemical spaces,
namely, phytochemicals in IMPPAT 1.0 and three collections of small
molecules obtained from Clemons et al.(52 (link)) corresponding to 6152 commercial compounds (CC),
5963 diversity-oriented synthesis compounds (DC’), and 2477
natural products (NP). For each compound in the above-mentioned five
chemical spaces, we computed using RDKit⁸⁸ two size-independent metrics, namely, stereochemical complexity,
which is the fraction of stereogenic carbon atoms in a compound, and
shape complexity, which is the ratio of sp³-hybridized
carbon atoms to the total number of sp²- and sp³-hybridized carbon atoms in a compound, and six other physicochemical
properties, namely, molecular weight, log P, topological polar surface
area, number of hydrogen bond donors, number of hydrogen bond acceptors,
and number of rotatable bonds.

Vivek-Ananth R.P., Mohanraj K., Sahoo A.K, & Samal A. (2023). IMPPAT 2.0: An Enhanced and Expanded Phytochemical Atlas of Indian Medicinal Plants. ACS Omega, 8(9), 8827-8845.

Publication 2023

Anabolism Carbon Donors Hydrogen Bonds Phytochemicals

Structural Analysis of Protein Complexes

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Publication 2023

Complement System Proteins Hydrogen Bonds Proteins Salts Sequence Alignment

Virtual Screening for OXA-23 Inhibitors

Virtual screening is the use of high-performance computing to analyze large datasets of chemical compounds to filter out the potential poses. The obtained compounds from the online sources are employed in the virtual screening workflow (Schrodinger LLC, New York, United States) against OXA-23. The screening was carried out step-by-step and the best docked poses are filtered using Glide score, Glide energy, and hydrogen bond interactions (Halgren et al., 2004 (link); Friesner et al., 2006 (link); Bochevarov et al., 2013 (link); Balajee et al., 2016 (link); Ramachandran et al., 2020 (link)).

Ramachandran B., Muthupandian S., Jeyaraman J, & Lopes B.S. (2023). Computational exploration of molecular flexibility and interaction of meropenem analogs with the active site of oxacillinase-23 in Acinetobacter baumannii. Frontiers in Chemistry, 11, 1090630.

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Publication 2023

Hydrogen Bonds

Protein Solubility in Selective Solvents

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Publication 2023

2-Mercaptoethanol Biuret Centrifugation Disulfides Hydrogen Bonds Hydrophobic Interactions Ions Proteins Sodium Chloride Solvents Urea

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Tools by AutoDock

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Maestro by Schrödinger

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Ligprep by Schrödinger

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LigPrep is a software tool designed to prepare chemical structures for computational modeling and analysis. It performs a variety of structure preparation tasks, including generating 3D molecular structures, adding hydrogen atoms, and adjusting ionization states. LigPrep is a useful tool for preparing chemical compounds for further computational studies.

Tools 1 by AutoDock

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Vina 1 by AutoDock

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AutoDock Vina 1.1.2 is a software application designed for molecular docking. It is capable of predicting the binding affinity and orientation of small molecules (ligands) to a given protein (receptor). The software uses a hybrid global-local search engine and a scoring function to evaluate the potential binding interactions between the ligand and the receptor.

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What are the different types of hydrogen bonds and how do they vary in strength?

Hydrogen bonds can be classified into several types based on the electronegativity of the atoms involved. Generally, the stronger the electronegativity difference between the hydrogen donor and acceptor, the stronger the hydrogen bond. For example, hydrogen bonds between fluorine and hydrogen are the strongest, followed by those involving oxygen, nitrogen, and finally chlorine. The bond strength can range from 1-40 kJ/mol, with the strongest being comparable to a weak covalent bond. Understanding these variations is crucial for accurately modeling and predicting molecular interactions in fields like structural biology and materials science.

How do hydrogen bonds contribute to the stability and structure of biomolecules like proteins and nucleic acids?

Hydrogen bonds play a pivotal role in stabilizing the three-dimensional structures of proteins and nucleic acids. In proteins, hydrogen bonds between the carbonyl oxygen and amide hydrogen atoms of the peptide backbone help maintain the alpha-helix and beta-sheet secondary structures. Additionally, hydrogen bonds between amino acid side chains can further stabilize the tertiary structure. In nucleic acids like DNA and RNA, the base pairing between complementary bases (A-T and G-C) is mediated by a network of hydrogen bonds, which is essential for maintaining the double-helix structure and facilitating important biological processes like replication and transcription.

What are some common challenges in accurately identifying and quantifying hydrogen bonds, and how can PubCompare.ai help address them?

Accurately identifying and quantifying hydrogen bonds can be challenging due to factors like bond distance thresholds, angular constraints, and the presence of water molecules or other competing interactions. Resesarchers often struggle to find the most reliable and reproducible protocols for hydrogen bond analysis from the vast literature. This is where PubCompare.ai can be invaluable. The platfrom's AI-driven comparisons allow you to effortlessly sceen protocol literature, pinpoint critical insights, and identify the most effective hydrogen bond protocols for your specific research needs. By leveraging this innovative tool, you can enhance research reproducibility and choose the most accurate and reliable methods for your hydrogen bond analysis, saving time and resources in the process.

How can hydrogen bonds be leveraged in the design of novel materials and drug molecules?

Hydrogen bonds are essential for the design and development of novel materials and drug molecules. In materials science, hydrogen bonds can be used to create self-assembling supramolecular structures with unique properties, such as stimuli-responsive hydrogels and organic electronics. By carefully engineering hydrogen bond patterns, researchers can tailor the mechanical, thermal, and optical characteristics of these materials. In drug design, hydrogen bonds play a crucial role in mediating the interactions between small-molecule drugs and their target proteins. Identifying and optimizing these non-covalent interactions is vital for improving drug potency, selectivity, and binding affinity, ultimately enhancing the efficacy and safety of pharmaceutical products. PubCompare.ai can assist in this process by helping researchers quickly find the best protocols for accurately analyzing and predicting hydrogen bond patterns in their molecular models and experimental data.

What are some common applications of hydrogen bond analysis in different scientific fields?

Hydrogen bond analysis has a wide range of applications across various scientific disciplines: In structural biology, hydrogen bond patterns are used to study the folding and stability of proteins, as well as their interactions with ligands and other biomolecules. This is crucial for understanding protein function and informing drug design efforts. In materials science, hydrogen bonds are leveraged to engineer self-assembling supramolecular structures with desired mechanical, thermal, and optical properties, such as in the development of stimuli-responsive materials. In environmental chemistry, hydrogen bonding plays a key role in the behavior and fate of pollutants, influencing their solubility, transport, and reactivity in aquatic and atmospheric systems. In nanotechnology, hydrogen bonds are exploited to create complex nanostructures and to study the interactons between nanomaterials and biological systems. Regardelss of the specific field, PubCompare.ai can help researchers optimize their hydrogen bond analysis by quickly identifying the most effective and reliable protocols from the literature, improving the reproducibility and accuracy of their findings.

More about "Hydrogen Bonds"

Hydrogen bonding is a crucial non-covalent interaction that plays a pivotal role in a wide range of biological and chemical processes.
These directional, electrostatic attractions between a hydrogen atom and an electronegative atom, such as oxygen or nitrogen, are essential for stabilizing molecular structures and mediating important intermolecular interactions.
Understanding and accurately identifying hydrogen bond patterns is vital for research in fields like structural biology, drug design, and materials science.
Researchers can utilize various tools and software to analyze and optimize hydrogen bond protocols.
The Protein Preparation Wizard in the Maestro suite, for example, can be used to prepare protein structures for further analysis, including the identification and evaluation of hydrogen bonds.
AutoDock Tools, a popular molecular docking software, also provides features for hydrogen bond analysis.
LigPrep, another tool in the Maestro suite, can be used to generate 3D molecular structures and analyze their hydrogen bonding patterns.
AutoDock Vina 1.1.2, a molecular docking program, can be used to study the hydrogen bonding interactions between ligands and receptors.
The Discovery Studio Visualizer and Discovery Studio software can also be leveraged for visualizing and analyzing hydrogen bond interactions, while PyMOL, a molecular graphics system, offers robust tools for visualizing and exploring hydrogen bonding networks.
PubCompare.ai, an innovative solution, empowers researchers to easily locate the best hydrogen bond protocols from literature, preprints, and patents using AI-driven comparisons.
This tool can help enhance research reproducibility by optimizing hydrogen bond analysis, making it easier than ever to identify the most accurate and reliable protocols.
By utilizing these tools and software, researchers can gain deeper insights into the role of hydrogen bonds in various biological and chemical processes, ultimately driving advancements in fields such as structural biology, drug design, and materials science.